Differences

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--- ai_edge_case_handling [2025/05/26 15:33] – [Class Overview] eagleeyenebula
+++ ai_edge_case_handling [2025/05/26 15:37] (current) – [Best Practices] eagleeyenebula
@@ Line 102: / Line 102: @@
 ==== Example 1: Validating a Data Source ====
-Use the `check_data_source_availability()` method to ensure that the specified data file exists before proceeding further in the pipeline.
+Use the **check_data_source_availability()** method to ensure that the specified data file exists before proceeding further in the pipeline.
-```python
+<code>
+python
 from ai_edge_case_handling import EdgeCaseHandler
 file_path = "data/dataset.csv"  # Replace with the actual file path
+</code>
-# Check if the file exists
+**Check if the file exists**
+<code>
 if EdgeCaseHandler.check_data_source_availability(file_path):
     print(f"Data source is available: {file_path}")
 else:
     print(f"Data source is not available: {file_path}")
-```
+</code>
 **Runtime Logs & Output:**
-*Case 1: File Exists*
+**Case 1: File Exists**
-*Case 2: File Does Not Exist*
----
+**Case 2: File Does Not Exist**
 ==== Example 2: Handling Missing Values with Mean Strategy ====
-The `handle_missing_values()` method allows you to fill missing values in a dataset using the mean of existing values.
+The **handle_missing_values()** method allows you to fill missing values in a dataset using the mean of existing values.
-```python
+<code>
-# Sample data with missing "value" fields
+python
+</code>
+**Sample data with missing "value" fields**
+<code>
 data = [
     {"id": 1, "value": 10},
@@ Line 136: / Line 136: @@
     {"id": 3, "value": 30},
 ]
+</code>
-# Handle missing values with the "mean" strategy
+**Handle missing values with the "mean" strategy**
+<code>
 cleaned_data = EdgeCaseHandler.handle_missing_values(data, strategy="mean")
 print(f"Cleaned Data: {cleaned_data}")
-```
+</code>
 **Logs & Output:**
----
 ==== Example 3: Removing Records with Missing Values ====
-Using the `remove` strategy, you can eliminate entries that contain missing values.
+Using the **remove** strategy, you can eliminate entries that contain missing values.
-```python
+<code>
+python
 # Handle missing values by removing incomplete records
 cleaned_data = EdgeCaseHandler.handle_missing_values(data, strategy="remove")
 print(f"Cleaned Data: {cleaned_data}")
-```
+</code>
 **Logs & Output:**
----
 ==== Example 4: Adding Custom Strategies ====
-Extend the `EdgeCaseHandler` class to define custom strategies for handling missing values.
+Extend the **EdgeCaseHandler** class to define custom strategies for handling missing values.
-```python
+<code>
+python
 class CustomEdgeCaseHandler(EdgeCaseHandler):
     @staticmethod
@@ Line 187: / Line 185: @@
 print(f"Cleaned Data (Custom): {cleaned_data}")
-```
+</code>
 **Logs & Output:**
----
 ===== Use Cases =====
 . **Data Validation Pipelines**:
-   - Ensure data pipelines are robust to file-system errors, missing files, and unavailable data sources.
+   * Ensure data pipelines are robust to file-system errors, missing files, and unavailable data sources.
 . **Preprocessing Missing Features**:
-   - Handle missing or incomplete feature values during feature engineering for machine learning models.
+   * Handle missing or incomplete feature values during feature engineering for machine learning models.
 . **Data Integrity Debugging**:
-   - Use extensive logging to identify problematic records or strategies causing anomalies in processing.
+   * Use extensive logging to identify problematic records or strategies causing anomalies in processing.
 . **Custom Cleaning Pipelines**:
-   - Extend the module with domain-specific strategies, such as interpolation or external API lookups, to handle missing information.
+   * Extend the module with domain-specific strategies, such as interpolation or external API lookups, to handle missing information.
----
 ===== Best Practices =====
 . **Validate Early**:
-   - Always validate data sources at the start of your pipeline to avoid unnecessary runtime errors.
+   * Always validate data sources at the start of your pipeline to avoid unnecessary runtime errors.
 . **Choose Appropriate Strategies**:
-   - Select missing value handling strategies based on the nature of your data and downstream requirements.
+   * Select missing value handling strategies based on the nature of your data and downstream requirements.
 . **Log Everything**:
-   - Use logging to track all edge case handling actions for accountability and debugging.
+   * Use logging to track all edge case handling actions for accountability and debugging.
 . **Modular Extensions**:
-   - Extend methods to handle unique edge case scenarios tailored to your domain or application.
+   * Extend methods to handle unique edge case scenarios tailored to your domain or application.
----
 ===== Conclusion =====